24 March 2016 Signal intensity analysis of ecological defined habitat in soft tissue sarcomas to predict metastasis development
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Abstract
Magnetic Resonance Imaging (MRI) is the standard of care in the clinic for diagnosis and follow up of Soft Tissue Sarcomas (STS) which presents an opportunity to explore the heterogeneity inherent in these rare tumors. Tumor heterogeneity is a challenging problem to quantify and has been shown to exist at many scales, from genomic to radiomic, existing both within an individual tumor, between tumors from the same primary in the same patient and across different patients. In this paper, we propose a method which focuses on spatially distinct sub-regions or habitats in the diagnostic MRI of patients with STS by using pixel signal intensity. Habitat characteristics likely represent areas of differing underlying biology within the tumor, and delineation of these differences could provide clinically relevant information to aid in selecting a therapeutic regimen (chemotherapy or radiation). To quantify tumor heterogeneity, first we assay intra-tumoral segmentations based on signal intensity and then build a spatial mapping scheme from various MRI modalities. Finally, we predict clinical outcomes, using in this paper the appearance of distant metastasis - the most clinically meaningful endpoint. After tumor segmentation into high and low signal intensities, a set of quantitative imaging features based on signal intensity is proposed to represent variation in habitat characteristics. This set of features is utilized to predict metastasis in a cohort of STS patients. We show that this framework, using only pre-therapy MRI, predicts the development of metastasis in STS patients with 72.41% accuracy, providing a starting point for a number of clinical hypotheses.
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Hamidreza Farhidzadeh, Baishali Chaudhury, Jacob G. Scott, Dmitry B. Goldgof, Lawrence O. Hall, Robert A Gatenby, Robert J. Gillies, Meera Raghavan, "Signal intensity analysis of ecological defined habitat in soft tissue sarcomas to predict metastasis development", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97851H (24 March 2016); doi: 10.1117/12.2216961; https://doi.org/10.1117/12.2216961
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